Differentiating Defects in Red Oak Lumber by Discriminant Analysis Using Color, Shape, and Density
Keywords:
Lumber scanning, defect detection, discriminant analysis, machine visionAbstract
Defect color, shape, and density measures aid in the differentiation of knots, bark pockets, stain/mineral streak, and clearwood in red oak, (Quercus rubra). Various color, shape, and density measures were extracted for defects present in color and X-ray images captured using a color line scan camera and an X-ray line scan detector. Analysis of variance was used to determine which color, shape, and density measures differed between defects. Discriminant classifiers were used to test which defect measures best discriminated between different defects in lumber.
The ANOVA method of model measure selection was unable to provide a direct method of selecting the optimum combination of measures; however, it did provide insight as to which measure should be selected in cases of confusion between defects. No single sensor measure provided overall classification accuracy greater than 70%, indicating the need for multisensor and multimeasure information for defect classification. When used alone, color measures resulted in the highest overall defect classification accuracy (between 69 and 70%). Shape and density measures resulted in the lowest overall classification accuracy (between 32 and 53%); however, when used in combination with other measures, they contributed to a 5-10% increase in defect classification accuracy. It was determined that defect classification required multisensor information to obtain the highest accuracy. For classifying defects in red oak, sensor measures should include two color mean values and two standard deviation values, a shape measure, and a X-ray standard deviation value.
References
Adel, M., D. Wolf, R. Vogrig, and R. Husson. 1993. Evaluation of colour spaces in computer vision application of wood defects detection. Pages 499-504 in Proc. IEEE International Conference on Systems, Man, and Cybernetics, v 2. IEEE Service Center, Piscataway, NJ.nAraman, P. A., D. L. Schmoldt, T. Cho, R. W. Conners, and D. E. Kline. 1992. Machine vision systems for processing hardwood lumber and logs. AI Applications6(2): 13-26.nÅstrand, E. 1992. Detection and classification of surface defects in wood: A preliminary study. LiTH-ISY-I-1437. Department of Electrical Engineering, Linköping University, Sweden.nBallard, D. H., and C. M. Brown. 1982. Computer vision. Prentice-Hall, Inc., Englewood Cliffs, NJ.nBond, B. H. 1998. Characterization of defects in wood using color, shape, and density parameters. Ph.D. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA.nBrunner, C. C., G. B. Shaw, D. A. Butler, and J. W. Funck. 1990. Using color in machine vision systems for wood processing. Wood Fiber Sci.22(4):413-428.nBuehlmann, U., J. K. Wiedenbeck, and D. E. Kline. 1997. Character-marked furniture potential for lumber yield increase in rip-first rough mills. Forest Prod. J. In Review.nCho, T. H. 1991. A knowledge-based machine vision system for automated industrial web inspection. Ph.D. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA.nConners, R. W., C. W. McMillin, and C. N. Ng. 1985. The utility of color information in the location and identification of defects in surfaced hardwood lumber. Pages XVIII-I-XVIII-33 in Proc. 1st International Conference on Scanning Technology in Sawmilling, San Francisco, CA.nConners, R. W., C. T. Ng, T. H. Drayer, J. G. Tront, D. E. Kline, and C. J. Gatchell. 1990. Computer vision system hardware system for automating rough mills of furniture plants. Pages 777-787 in Proc. SPIE, Applications of Artificial Intelligence, VIII. Orlando, FL.nConners, R. W., T. H. Cho, C. T. Ng, T. H. Drayer, P. A. Araman, and R. L. Brisbin. 1992. A machine vision system for automatically grading hardwood lumber. Industrial Metrology2(3/4):317-342.nConners, R. W., D. E. Kline, P. A. Araman, and T. H. Drayer. 1997. Machine vision technology for the forest products industry. IEEE Computer Innovative Technology for Computer Professionals30(7):43-48.nDuda, R. D., and P. E. Hart. 1973. Pattern classification and scene analysis. John Wiley and Sons, New York, NY.nEveritt, B. S., and G. Dunn. 1992. Applied multivariate data analysis. Oxford University Press, New York, NY.nForrer, J. B., D. A. Butler, J. W. Funck, and C. C. Brunner. 1988. Image sweep-and-mark algorithms. Part 1. Basic algorithms. Forest Prod. J.38(11/12):75-79.nGrum, F., and C. J. Bartleson. Ed. 1980. Color measurement. Academic Press, New York, NY.nHagman, P. O. G., and S. Grundberg. 1993. Multivariate image analysis methods to classify features on Scots pine: Evaluation of a multi-sensor approach. In R. Szymani. ed. 5th International Conference on Scanning Technology and Process Control for the Wood Products Industry, October 25-27, 1993, Atlanta, GA. 18 pp.nHand, D. J. 1981. Discrimination and classification. John Wiley and Sons, New York, NY.nHand, D. J. 1986. Recent advances in error estimation. Pattern Recognition Letters4:335-346.nHuberty, C. J. 1994. Applied discriminant analysis. John Wiley and Sons, New York, NY.nKauppinen, H., and O. Silven. 1995. Development of color-vision-based solutions for lumber grading. Pages 203-214 in Proc. SPIE-The-International-Society-for-Optical-Engineering. v 2423. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA.nKline, D. E., R. W. Conners, D. L. Schmoldt, P. A. Araman, and R. L. Brisbin. 1993. A multiple sensor machine vision system for automatic hardwood feature detection. In R. Szymani, ed. 5th International Conference on Scanning Technology and Process Control for the Wood Industry. October 25-27, Atlanta, GA. 11 pp.nLampinen, J., and S. Smolander. 1996. Self-organizing feature extraction in recognition of wood surface defects and color images. Int. J. Pattern Recognition and Artificial Intelligence10(2):97-113.nMedia Cybernetics. 1995. Image Pro Plus Version 1.3 for Windows. Silver Springs, MD.nNational Hardwood Lumber Association (NHLA). 1994. Rules for the measurement and inspection of hardwood and cypress. Memphis, TN.nOtt, L. 1988. An introduction to statistical methods and data analysis. 3rd ed. PWS-Kent Publishing Company, Boston, MA.nPolzleitner, W., and G. Schwingshakl. 1990. Real-time classification of wooden boards. SPIE. High-speed Inspection Architectures, Barcoding, and Character Recognition1384:38-39.nPortala, J. F., and J. Ciccotelli. 1992. Nondestructive testing techniques applied to wood scanning. Industrial Metrology2(3/4):299-307.nSas Institute. 1996. SAS Users Guide. Cary, N.C.nSilvén, S., and H. Kauppinen. 1996. Recent developments in wood inspection. Int. Pattern Recognition and Artificial Intelligence10(1):83-95.nWeeks, A. R. Jr. 1996. Fundamentals of electronic image processing. SPIE. Washington, DC.nWiedenbeck, J., and U. Buehlmann. 1995. Character-marked furniture: The yield picture. In Proc. 1995 Hardwood Research Symposium, National Hardwood Lumber Association. 13 pp.n
Downloads
Published
Issue
Section
License
The copyright of an article published in Wood and Fiber Science is transferred to the Society of Wood Science and Technology (for U. S. Government employees: to the extent transferable), effective if and when the article is accepted for publication. This transfer grants the Society of Wood Science and Technology permission to republish all or any part of the article in any form, e.g., reprints for sale, microfiche, proceedings, etc. However, the authors reserve the following as set forth in the Copyright Law:
1. All proprietary rights other than copyright, such as patent rights.
2. The right to grant or refuse permission to third parties to republish all or part of the article or translations thereof. In the case of whole articles, such third parties must obtain Society of Wood Science and Technology written permission as well. However, the Society may grant rights with respect to Journal issues as a whole.
3. The right to use all or part of this article in future works of their own, such as lectures, press releases, reviews, text books, or reprint books.
